PRPF40A induces inclusion of exons in GC-rich regions important for human myeloid cell differentiation

Abstract

We characterized the regulatory mechanisms and role in human myeloid cell survival and differentiation of PRPF40A, a splicing factor lacking a canonical RNA Binding Domain. Upon PRPF40A knockdown, HL-60 cells displayed increased cell death, decreased proliferation and slight differentiation phenotype with upregulation of immune activation genes. Suggestive of both redundant and specific functions, cell death but not proliferation was rescued by overexpression of its paralog PRPF40B. Transcriptomic analysis revealed the predominant role of PRPF40A as an activator of cassette exon inclusion of functionally relevant splicing events. Mechanistically, the exons exclusively upregulated by PRPF40A are flanked by short and GC-rich introns which tend to localize to nuclear speckles in the nucleus center. These PRPF40A regulatory features are shared with other splicing regulators such as SRRM2, SON, PCBP1/2, and to a lesser extent TRA2B and SRSF2, as a part of a functional network that regulates splicing partly via co-localization in the nucleus.

Graphical Abstract

Figure 1.

Phenotypic characterization of PRPF40A/B knockdown HL-60 cells. (A) PRPF40A and PRPF40B expression in blood cells and other tissues (the leukemia data in Supplementary Figure S1B). Data obtained from the GTEx database. (B) Cell viability MTS assay relative to wildtype HL-60 showed that PRPPF40A-KD HL-60 cells were less proliferative. The statistical significance was calculated against SCR using the Student's t-test and corrected with the Benjamini–Hochberg method. (C) Increased cell death in PRPF40A-KD HL-60 cells was also observed with AnnexinV/PI apoptosis assay. This was rescued with the overexpression of PRPF40B. The statistical significance was calculated between the knockdown samples (empty vector) against the scrambled control (empty vector) or the corresponding PRPF40B overexpression samples using the Welch's t-test and corrected with the Benjamini–Hochberg method. (D) Representative density plot of Annexin V (FITC) versus PI. (E) Giemsa stain of wildtype HL-60 and knockdown sublines after doxycycline induction. (F) Cell cycle analysis with Hoechst 33342 and flow cytometry likewise revealed an increased proportion of cells in the G1 phase upon PRPF40A-KD (highlighted in the red box), but PRPF40B overexpression did not significantly improve proliferation. The statistical significance was calculated between the knockdown samples (empty vector) against the scrambled control or the corresponding PRPF40B overexpression sample using the Welch's t-test and corrected with the Benjamini–Hochberg method. No statistical significance was found between corresponding empty vector controls and PRPF40B overexpression samples. (G) Flow cytometry analysis of myeloid cell markers showed increased CD86 expression in PRPF40A knockdown HL-60 cells. The statistical significance in all panels is presented as *P< 0.05, **P< 0.01.

Figure 2.

Phenotypic effects of the depletion of PRPF40A in HL-60 myeloid cells. (A) Flow cytometry data of surface markers for wildtype HL-60 and knockdown sublines upon doxycycline induction followed by differentiation with VD3 or ATRA/DMSO for 4 days. Representative histogram plots of CD11b (FITC), CD86 (PE) and CD14 (APC), with isotype controls represented by the black curves. The mean percentage of marker-positive cells is indicated in the histograms. (B) Cell viability obtained with 7-AAD staining. (C) CD11b and CD14 expression of CD86-low/high population after VD3 treatment. (D) Relative density plot of CD11b versus CD86 for ATRA/DMSO treated cells. The statistical significance depicted in all panels was calculated against the respective SCR controls using the Student's t-test and corrected with Benjamini–Hochberg method. The statistical significance in all panels is presented as *P< 0.05), **P< 0.01.

Figure 3.

Differential gene expression and splicing upon PRPF40A depletion without or with PRPF40B rescue. (A, B) Baseline percent spliced in (PSI) values of cassette exons plotted against the difference in PSI (ΔPSI) after PRPF40A-KD or rescue with PRPF40B-OE respectively. The density plots above indicate the distribution of points along the X-axis. (C–E) Distribution of differential cassette exons, alternate splice usage or intron retention between the two indicated datasets. The p-values for the overlap between the two datasets was calculated using the Fisher's exact test. (F, G) M–A plot (log-ratio versus mean expression) of differentially expressed genes for the indicated datasets. The dashed lines indicate thresholds. Only the statistically significant DEGs are highlighted in colour. (H) Distribution of DEGs between both datasets. The P-values for the overlap between the two datasets was calculated using the Fisher's exact test. (I) Distribution of DEGs which are also differentially spliced.

Figure 4.

Enriched pathways and functions for the expression and splicing targets of PRPF40A. (A) Details of the Gene Ontology (GO) genesets selected for analysis of DEGs in the PRPF40A-KD (A-KD) dataset. DSGs: differentially spliced genes, DEGs that are also differentially spliced. The rescued genes refer to DEGs whose changes upon PRPF40A-KD were dampened by PRPF40B-OE. (B) Network plot of DEGs in the GO genesets indicated in Figure 4A. DEGs with concomitant differential splicing are indicated by hollow circles, while a black border indicates that the DEG was rescued by PRPF40B overexpression. (C) Distribution of cassette exon types by annotation for coding changes as in the key below, in addition to the percentage of differentially spliced cassette exons that are annotated as alternative exons in the GENCODE V43. (D) The three most numerous protein feature types suppressed by PRPF40A-KD, as reported by NEASE. PH: Pleckstrin homology domain. (E) The top 40 cassette exons with encoded protein features that are downregulated upon PRPF40A depletion, ranked by the number of known domain-domain interactions. Red borders indicate that the splicing event was rescuable by PRPF40B overexpression.

Figure 5.

Features of PRPF40A-regulated alternative splicing events in HL-60. (A–C) Distribution of intronic and exonic lengths of each differentially spliced cassette exon subset, with green and red arrows indicating up- and downregulation in the corresponding dataset. Mean values are indicated by the red diamond. The statistical significance between the background and each subset was calculated using the Wilcoxon's rank-sum test and corrected using the Benjamini–Hochberg method. The sample sizes are indicated below each violin plot. (D) Ternary plots of the nucleotide content of the 3′ss polypyrimidine tracts (18nt) for each indicated dataset. The black and red points indicate the mean values for the background and RNA-seq datasets, respectively. (E) Rolling window average (window of 5nt) plot of GC content for the indicated cassette exon subsets. The background values are indicated in dashed lines. (F, G) GC content of introns located up- and downstream of each cassette exon. The statistical significance within each group was calculated using the Kruskal–Wallis test within each subgroup (indicated by dashed lines) and corrected using the Benjamini–Hochberg method. The sample sizes are indicated below each violin plot. Int. len.: intronic length. The statistical significance in all panels is presented as *P< 0.05, ***P< 0.001, ****P< 0.0001. The arrows next to the asterisks indicate the direction of median change relative to background values.

Figure 6.

Comparative meta-analysis reveals a subset of splicing factors with a common pattern of GC-rich splicing targets. (A) Overlap of differential cassette exons between each dataset and the PRPF40A/B target subsets, including exons downregulated by PRPF40A-KD and not rescued, downregulated exons rescued by PRPF40B-OE, and exons upregulated by PRPF40A-KD. The ‘+’ and ‘–’ indicate the exons that are positively and negatively associated with the expression of each splicing factor. The positively associated events are either upregulated in RBP overexpression datasets or downregulated in RBP knockdown datasets, while negatively associated events show the opposite trend. (B) Distribution of upstream intronic GC content for all human cassette exons (grey), positively correlated exons (+, orange) and negatively correlated exons (-, blue). The statistical significance within each group was calculated using the Wilcoxon's rank-sum test. **P< 0.01, ***P< 0.001, ****P< 0.0001. (C–E) Immunofluorescence assays for the localisation of the indicated splicing factors. The outlines indicate the DAPI-stained nuclear area. (F) Casilio-based tagging of genomic loci corresponding to exons flanked by lower GC content (AASDH Exon 12) and higher GC content (FES Exon 3). The concentric rings indicate the boundary of each radial scope, with the distribution of points across different cells indicated in the bar plot. For each gene, we summed up the loci counts across both gRNAs to get overall loci distributions, and applied the Kolmogorov–Smirnov test for the statistical differences between the AASDH and FES distributions (P-value as indicated).